Parameter Learning Algorithm of Fuzzy Logic System with Noisy Inputs

The parameters can not strongly converge to the true values when using traditional least squares cost function with noisy input data. This problem can be solved by a novel cost function which contains error variables. The cost function is extended to multi input single output system, and the error variables are obtained through learning algorithm to avoid repeated measurement. The simulation results show the efficiency of this algorithm.